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---
license: llama3
language:
- en
pipeline_tag: image-text-to-text
tags:
- text-generation-inference
extra_gated_fields:
First Name: text
Last Name: text
Country: country
Affiliation: text
I want to use this model for:
type: select
options:
- Research
- Education
- label: Other
value: Other
I agree to use this model in accordance to META LLAMA 3 COMMUNITY LICENSE AGREEMENT and to not use this model for commercial purposes: checkbox
---
# Dragonfly-Med Model Card
**Note: Users are permitted to use this model in accordance with the Llama 3 Community License Agreement. Additionally, due to the licensing restrictions of the dataset used to train this model, which prohibits commercial use, the Dragonfly-Med model is restricted to non-commercial use only.**
## Model Details
Dragonfly-Med is a multimodal biomedical visual-language model, trained by instruction tuning on Llama 3.
- **Developed by:** [Together AI](https://www.together.ai/)
- **Model type:** An autoregressive visual-language model based on the transformer architecture
- **License:** [Llama 3 Community License Agreement](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
- **Finetuned from model:** [Llama 3](https://github.com/meta-llama/llama3)
### Model Sources
- **Repository:** https://github.com/togethercomputer/Dragonfly
- **Blog:** https://www.together.ai/blog/dragonfly-v1
- **Paper:** https://arxiv.org/abs/2406.00977
## Uses
The primary use of Dragonfly-Med is research on large visual-language models.
It is primarily intended for researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
### 💿 Installation
Create a conda environment and install necessary packages
```bash
conda env create -f environment.yml
conda activate dragonfly_env
```
Install flash attention
```bash
pip install flash-attn --no-build-isolation
```
As a final step, please run the following command.
```bash
pip install --upgrade -e .
```
### 🧠 Inference
If you have successfully completed the installation process, then you should be able to follow the steps below.
Question: Provide a brief description of the given image.
Load necessary packages
```python
import torch
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer
from dragonfly.models.modeling_dragonfly import DragonflyForCausalLM
from dragonfly.models.processing_dragonfly import DragonflyProcessor
from pipeline.train.train_utils import random_seed
```
Instantiate the tokenizer, processor, and model.
```python
device = torch.device("cuda:0")
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-Med-v1")
clip_processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
image_processor = clip_processor.image_processor
processor = DragonflyProcessor(image_processor=image_processor, tokenizer=tokenizer, image_encoding_style="llava-hd")
model = DragonflyForCausalLM.from_pretrained("togethercomputer/Llama-3-8B-Dragonfly-Med-v1")
model = model.to(torch.bfloat16)
model = model.to(device)
```
Now, lets load the image and process them.
```python
image = Image.open("ROCO_04197.jpg")
image = image.convert("RGB")
images = [image]
# images = [None] # if you do not want to pass any images
text_prompt = "<|start_header_id|>user<|end_header_id|>\n\nSummarize the visual content of the image.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
inputs = processor(text=[text_prompt], images=images, max_length=2048, return_tensors="pt", is_generate=True)
inputs = inputs.to(device)
```
Finally, let us generate the responses from the model
```python
temperature = 0
with torch.inference_mode():
generation_output = model.generate(**inputs, max_new_tokens=1024, eos_token_id=tokenizer.encode("<|eot_id|>"), do_sample=temperature > 0, temperature=temperature, use_cache=True)
generation_text = processor.batch_decode(generation_output, skip_special_tokens=False)
```
An example response.
```plaintext
Computed tomography scan showing a large heterogenous mass in the pelvis<|eot_id|>
```
## Training Details
See more details in the "Implementation" section of our [paper](https://arxiv.org/abs/2406.00977).
## Evaluation
See more details in the "Results" section of our [paper](https://arxiv.org/abs/2406.00977).
## 🏆 Credits
We would like to acknowledge the following resources that were instrumental in the development of Dragonfly:
- [Meta Llama 3](https://huggingface.co/meta-llama/Meta-Llama-3-8B): We utilized the Llama 3 model as our foundational language model.
- [CLIP](https://huggingface.co/openai/clip-vit-base-patch32): Our vision backbone is CLIP model from OpenAI.
- Our codebase is built upon the following two codebases:
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://github.com/Luodian/Otter)
- [LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images](https://github.com/thunlp/LLaVA-UHD)
## 📚 BibTeX
```bibtex
@misc{chen2024dragonfly,
title={Dragonfly: Multi-Resolution Zoom Supercharges Large Visual-Language Model},
author={Kezhen Chen and Rahul Thapa and Rahul Chalamala and Ben Athiwaratkun and Shuaiwen Leon Song and James Zou},
year={2024},
eprint={2406.00977},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Model Card Authors
Rahul Thapa, Kezhen Chen, Rahul Chalamala
## Model Card Contact
Rahul Thapa ([email protected]), Kezhen Chen ([email protected]) |